MOre docs updates

README.md

@@ -23,10 +23,11 @@
 
 cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.
 
-**Please note** that cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
-1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
-2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
-3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
+> [!note]
+> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
+> 1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
+> 2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
+> 3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
 
 ## Installing cuVS
 

docs/source/working_with_ann_indexes.rst

@@ -1,102 +1,11 @@
 Working with ANN Indexes
 ========================
 
-- `Building an index`_
-- `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
-
-Building an index
------------------
-
-C
-^
-
-.. code-block:: c
-
-    #include <cuvs/neighbors/cagra.h>
-
-    cuvsResources_t res;
-    cuvsCagraIndexParams_t index_params;
-    cuvsCagraIndex_t index;
-
-    DLManagedTensor *dataset;
-    load_dataset(dataset);
-
-    cuvsResourcesCreate(&res);
-    cuvsCagraIndexParamsCreate(&index_params);
-    cuvsCagraIndexCreate(&index);
-
-    cuvsCagraBuild(res, index_params, dataset, index);
-
-    cuvsCagraIndexDestroy(index);
-    cuvsCagraIndexParamsDestroy(index_params);
-    cuvsResourcesDestroy(res);
-
-C++
-^^^
-
-.. code-block:: c++
-
-    #include <cuvs/neighbors/cagra.hpp>
-
-    using namespace cuvs::neighbors;
-
-    raft::device_matrix_view<float> dataset = load_dataset();
-    raft::device_resources res;
-
-    cagra::index_params index_params;
-
-    auto index = cagra::build(res, index_params, dataset);
-
-
-Python
-^^^^^^
-
-.. code-block:: python
-
-    from cuvs.neighbors import cagra
-
-    dataset = load_data()
-    index_params = cagra.IndexParams()
-
-    index = cagra.build_index(build_params, dataset)
-
-Rust
-^^^^
-
-.. code-block:: rust
-
-    use cuvs::cagra::{Index, IndexParams};
-    use cuvs::{Resources, Result};
-
-    use ndarray_rand::rand_distr::Uniform;
-    use ndarray_rand::RandomExt;
-
-    /// Example showing how to index and search data with CAGRA
-    fn cagra_example() -> Result<()> {
-        let res = Resources::new()?;
-
-        // Create a new random dataset to index
-        let n_datapoints = 65536;
-        let n_features = 512;
-        let dataset =
-            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
-
-        // build the cagra index
-        let build_params = IndexParams::new()?;
-        let index = Index::build(&res, &build_params, &dataset)?;
-
-        Ok(())
-    }
-
-
-Searching an index
-------------------
-
-
-CPU/GPU interoperability
-------------------------
-
-Serializing an index
---------------------
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   working_with_ann_indexes_c.rst
+   working_with_ann_indexes_cpp.rst
+   working_with_ann_indexes_python.rst
+   working_with_ann_indexes_rust.rst

docs/source/working_with_ann_indexes_c.rst

@@ -0,0 +1,39 @@
+Working with ANN Indexes in C
+=============================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: c
+
+    #include <cuvs/neighbors/cagra.h>
+
+    cuvsResources_t res;
+    cuvsCagraIndexParams_t index_params;
+    cuvsCagraIndex_t index;
+
+    DLManagedTensor *dataset;
+    load_dataset(dataset);
+
+    cuvsResourcesCreate(&res);
+    cuvsCagraIndexParamsCreate(&index_params);
+    cuvsCagraIndexCreate(&index);
+
+    cuvsCagraBuild(res, index_params, dataset, index);
+
+    cuvsCagraIndexDestroy(index);
+    cuvsCagraIndexParamsDestroy(index_params);
+    cuvsResourcesDestroy(res);
+
+
+Searching an index
+------------------
+
+
+Serializing an index
+--------------------

docs/source/working_with_ann_indexes_cpp.rst

@@ -0,0 +1,35 @@
+Working with ANN Indexes in C++
+===============================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: c++
+
+    #include <cuvs/neighbors/cagra.hpp>
+
+    using namespace cuvs::neighbors;
+
+    raft::device_matrix_view<float> dataset = load_dataset();
+    raft::device_resources res;
+
+    cagra::index_params index_params;
+
+    auto index = cagra::build(res, index_params, dataset);
+
+
+
+Searching an index
+------------------
+
+
+CPU/GPU interoperability
+------------------------
+
+Serializing an index
+--------------------

docs/source/working_with_ann_indexes_python.rst

@@ -0,0 +1,30 @@
+Working with ANN Indexes in Python
+==================================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: python
+
+    from cuvs.neighbors import cagra
+
+    dataset = load_data()
+    index_params = cagra.IndexParams()
+
+    index = cagra.build_index(build_params, dataset)
+
+
+Searching an index
+------------------
+
+
+CPU/GPU interoperability
+------------------------
+
+Serializing an index
+--------------------

docs/source/working_with_ann_indexes_rust.rst

@@ -0,0 +1,43 @@
+Working with ANN Indexes in Rust
+================================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: rust
+
+    use cuvs::cagra::{Index, IndexParams};
+    use cuvs::{Resources, Result};
+
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    /// Example showing how to index and search data with CAGRA
+    fn cagra_example() -> Result<()> {
+        let res = Resources::new()?;
+
+        // Create a new random dataset to index
+        let n_datapoints = 65536;
+        let n_features = 512;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        // build the cagra index
+        let build_params = IndexParams::new()?;
+        let index = Index::build(&res, &build_params, &dataset)?;
+
+        Ok(())
+    }
+
+
+Searching an index
+------------------
+
+
+Serializing an index
+--------------------